Exam Certified Machine Learning Professional topic 1 question 48 discussion

Actual exam question from Databricks's Certified Machine Learning Professional

Question #: 48
Topic #: 1

[All Certified Machine Learning Professional Questions]

A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

A. mlflow.register_model
B. MlflowClient.update_registered_model
C. mlflow.add_model_version
D. MlflowClient.get_model_version
E. The machine learning engineer needs to create an entirely new MLflow Model Registry model

Show Suggested Answer

Suggested Answer: A 🗳️

by BokNinja at Dec. 19, 2023, 1:39 a.m.

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saha3333

6 months, 3 weeks ago

Selected Answer: A

it automatically add a new version

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Mircuz

12 months ago

Selected Answer: A

update_registered_model allows you to update model description, the right choice should be A

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64934ca

12 months ago

Strike that, it's A...we're registering a new model, not updating the existing.

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64934ca

12 months ago

Selected Answer: B

Because the model name already exists, meaning it's registered already, and it's being refreshed: per docs: Updates metadata for RegisteredModel entity. Input field description should be non-None. Backend raises exception if a registered model with given name does not exist.

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yunyunqian

1 year, 1 month ago

I think the model is already registered, the answer should be B

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hugodscarvalho

1 year, 5 months ago

Selected Answer: A

mlflow.register_model will create a new model version in the model registry for the model files specified by model_uri. Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model

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BokNinja

1 year, 6 months ago

Answer is A import mlflow model_uri = "runs:/d16076a3ec534311817565e6527539c0/model" model_details = mlflow.register_model( model_uri=model_uri, name="project" )

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